Sunday, December 9, 2007

The following table lists the probabilities that the home team would win each game given the rushing averages, passing averages (includes sack yards lost and sacks as pass attempts), sack rates, third down conversion rates, penalty yards, and interception and fumble rates (by number of completions and rushing plays) for both teams. A logistic regression model trained on 1996-2006 data computes the probabilities. The final score margin is Home team points - Away team points. The sum of these probabilities of victory form the expected win totals in the second table after the jump. The tables will be updated as more box scores come in.

Home Team

Away Team

P(Home Team Won) (%)

Final Score Margin

WAS

CHI

90.4273

8

BUF

MIA

99.9455

21

DEN

KC

99.9756

34

SF

MIN

1.9545

-20

NE

PIT

76.8998

21

SEA

ARI

99.0068

21

CIN

STL

98.4705

9

DET

DAL

81.9878

-1

GB

OAK

99.8651

31

HOU

TB

65.8002

14

JAX

CAR

99.6201

31

PHI

NYG

27.0608

-3

TEN

SD

47.2990

-6

NYJ

CLE

18.8732

-6

Teams that hould Have Won but Didn'tDetroit Lions (vs. Cowboys), Win Prob = 81.9878% Dallas lost only one of their 3 fumbles. Other than that, Detroit was more efficient passing (6.4 ypp vs 6.0) but less efficient running (4.8 vs 5.4 ypc).

2 comments:

Derek-How do you calculate projected wins? Does it consider wins already under the belt? For example, you've got NE winning only 0.3 more games for the rest of the year. I'd guess that since you've got them at 10.8 current expected wins, you really expect them to win 2.5 more games for a total of 15.5.

Nope, not at all. Keep in mind, there's an average error of 1-2 games, so in reality, New England will win 14-15 games, but they will have outperformed their expectation somewhat.

Each game is assigned an expected win value for each team which is essentially the probability that team won the game given the box score. The expected win total projections you see are the sums of those probabilities project out to a 16-game season.

New England had 2 games that they "shouldn't" have won according to that probability: @IND (based on penalties) and @BAL (based on run, pass stats and other things). This essentially means the New England isn't perfect and is unlikely to go perfect. It's a way of limiting the effects of having one really bad game or one game where you keep running up the score *ahem*.

Most of the time, recovered fumbles are the difference between winning and losing (though teams are still penalized for it). For Chicago, it's been entirely Devin Hester. Other times, it's the timing of the turnovers. Interception rates have some dependence on situation, but it's still random when these incidents occur. If it's at your own 10 yard line, bad things will happen. If it's at their 30 yard line, then perhaps not.

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About the Author

My degree is in computer science, and the football research started as an independent study in artificial neural networks. As a lifelong NFL fan, I wanted to explore the relative importance of different factors in winning games. Since the research is still nascent, I wanted to put it out in the public domain and hopefully find others interested in teaming up. Once it becomes profitable, though... I just hope the mafia families running Vegas don't come to hurt me.